Antimicrobial peptides (AMPs) are a promising alternative for combating bacterial drug resistance. While current computer prediction models excel at binary classification of AMPs based on sequences, there is a lack of regression methods to accurately quantify AMP activity against specific bacteria, making the identification of highly potent AMPs a challenge. Here, we present a deep learning method, BERT-AmPEP60, based on the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) architecture to extract embedding features from input sequences. Using the transfer learning strategy, we built regression models to predict the minimum inhibitory concentration (MIC) of peptides for Escherichia coli (EC) and Staphylococcus aureus (SA). In five independent experiments with 10% leave-out sequences as the test sets, the optimal EC and SA models outperformed the state-of-the-art regression method and traditional machine learning methods, achieving an average mean squared error of 0.2664 and 0.3032 (log μM), respectively. They also showed a Pearson correlation coefficient of 0.7955 and 0.7530, and a Kendall correlation coefficient of 0.5797 and 0.5222, respectively. Our models outperformed existing deep learning and machine learning methods that rely on conventional sequence features. This work underscores the effectiveness of utilizing BERT with transfer learning for training quantitative AMP prediction models specific for different bacterial species. The web server of BERT-AmPEP60 can be found at https://app.cbbio.online/ampep/home. To facilitate development, the program source codes are available at https://github.com/janecai0714/AMP_regression_EC_SA.

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